No abstract

Recent years have seen major advances in Artificial Intelligence (AI) methods for environment perception in intelligent transportation systems. Although most of them have been achieved in the automotive sector there is a similar demand in the railway domain. This paper investigates Deep Neural Network (DNN) based environment perception using vehicle-borne camera images from the rail domain. Specifically, railway switch detection and classification are addressed as a relevant example for a DNN application with potential use for landmark positioning, environment perception, and condition monitoring. The lack of large training data sets in the railway sector (in contrast to the automotive domain) is compensated by an appropriate DNN architecture, an anchor box ratio optimization scheme, and transfer learning. The presented experimental results advocate for the adopted approach.

Self-driving cars need to be able to assess and understand the state of their surroundings. To achieve this goal, it is necessary to construct a model which holds information about the state of the environment based on sensor measurements. In common state estimation systems like Kalman filters, it is necessary to explicitly model state transitions and the observation process. These models have to match the internal dynamics of the observed system as closely as possible to yield reliable estimation results. In this work, we propose a method that can learn an approximation of the internal dynamics of a system, without the need to explicitly model these processes. Our system even works on highly complex data like frames of a video sequence. The approach is based on a latent variable model with a continuous hidden state space. To deal with the fact that the estimated processes are sequential, we use recurrent neural networks. As an example to show the potential of this system, resulting predicted future frames of short video sequences are shown. The proposed system shows a general approach for state estimation without the need for any knowledge about the underlying state transition or observation processes.